Reduction of N<sub>2</sub> to NH<sub>3</sub> by TiO<sub>2</sub>-supported Ni cluster catalysts: a DFT study
Bibliographic record
Abstract
Electrochemical techniques for ammonia synthesis are considered as an encouraging energy conversion technology to efficiently meet the challenge of nitrogen cycle balance. Herein, we find that TiO2(101)-supported Ni4 and Ni13 clusters can serve as efficient catalysts for electrocatalytic N2 reduction based on theoretical calculations. Electronic property calculations reveal the formation of electron-deficient Ni clusters on the TiO2 surface, which provides multiple active sites for N2 adsorption and activation. Theoretical calculation identifies the strongest activated configuration of N2* on the catalysts and confirms the potential-limiting step in the nitrogen reduction reaction (NRR). On Ni4-TiO2(101), N2* → NNH* is the potential-limiting step with a very small free energy increase (ΔG) of 0.24 eV (the corresponding overpotential is 0.33 V), while on Ni13-TiO2(101) the potential-limiting step occurs at NH* → NH2* with ΔG of 0.49 eV (the corresponding overpotential is 0.58 V). Moreover, the Nin-TiO2(101) catalyst, especially Ni13-TiO2(101), involves in a highly selective NRR even at the corresponding NRR overpotential. This work will enlighten material design to construct metal oxide supported transition metal clusters for the highly efficient NRR and NH3 synthesis.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".